The recent technological advances related to computing, storage, cloud, networking and the unstoppable deployment of end-user devices, are all coining the so-called Internet of Things (IoT). IoT embraces a wide set of heterogeneous services in highly impacting societal sectors, such as Healthcare, Smart Transportation or Media\ud delivery, all of them posing a diverse set of requirements, including real time response, low latency, or high capacity. In order to properly address such diverse set of requirements, the combined use of Cloud and Fog computing turns up as an emerging trend. Indeed, Fog provides low delay for services demanding real time response, constrained to support low capacity queries, whereas Cloud provides high capacity at the cost of a higher latency. It is with no doubt that a\ud new strategy is required to ease the combined operation of cloud and fog infrastructures in IoT scenarios, also referred to as Combined Fog-Cloud (CFC), in terms of service execution performance metrics. To that end, in this paper, we introduce and formulate the QoS-aware service allocation problem for CFC architectures as an integer optimization problem, whose solution minimizes the latency experienced by the services while guaranteeing the fulfillment of the\ud capacity requirements.Peer ReviewedPostprint (published version

The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT services.

No abstract

The need to extend the features of Cloud computing to the edge of the network has fueled the development of new computing architectures, such as Fog computing. When put together, the combined and continuous use of fog and cloud computing, lays the foundation for a new and highly heterogeneous computing ecosystem, making the most out of both, cloud and fog. Incipient research efforts are devoted to propose a management architecture to properly manage such combination of resources, such as the reference architecture proposed by the OpenFog Consortium or the recent Fog-to-Cloud (F2C). In this paper, we pay attention to such a combined ecosystem and particularly evaluate the potential benefits of F2C in dynamic scenarios, considering computing resources mobility and different traffic patterns. By means of extensive simulations we specifically study the aspects of service response time, network bandwidth occupancy, power consumption and service disruption probability. The results indicate that a combined fog-to-cloud architecture brings significant performance benefits in comparison with the traditional standalone Cloud, e.g., over 50% reduction in terms of power consumption.

Future IoT services execution may benefit from combining resources at cloud and at the edge. To that end, new architectures should be proposed to handle IoT services in a coordinated way at either the edge of the network, the cloud, or both. Reacting to that need, the Fog-to-Cloud concept has been recently proposed. A key aspect in the F2C design refers to security, since F2C raises security issues besides those yet unsolved in fog and cloud. Thus, we envision the need for new security strategies to handle all components in the F2C architecture. In this paper we propose an SDNbased (mater/slave) security architecture leveraging a centralized controller on the cloud, and distributed controllers at the edge of the network. We argue that the proposed architecture brings more security and privacy to the cloud users by reducing the distance between them and, therefore, reducing the risks of the so called man-in-the-middle attacks. The proposed security architecture is analyzed in some critical infrastructure scenarios in order to illustrate their potential benefits.

The increasing number of end user devices at the edge of the network, along with their ever increasing computing capacity, as well as the advances in Data Center technologies, paved the way for the generation of Internet of Things (IoT). Several IoT services have been deployed leveraging Cloud Computing and, more recently, Fog Computing. In order to enable efficient control of cloud and fog premises, Fog-to-Cloud (F2C) has been recently proposed as a distributed architecture for coordinated management of both fog and cloud resources. Certainly, many challenges remain unsolved in combined Fog-to-Cloud systems, mostly driven by the dynamicity and volatility imposed by edge devices, such as the recovery of failures at the edge of the network. Indeed, possible failures in computing commodities may be prohibitive for the achievement of the envisioned performance in F2C systems. In this work, we assess proactive and reactive strategies for failure recovery of network elements by modelling them as a Multidimensional Knapsack Problem (MKP) and study the impact of each one on several aspects such as service allocation time, recovery delay and computing resources load. The obtained results show the effect each strategy brings, thus concluding with some analysis on the recovery strategy best suiting distinct IoT scenarios.

No abstract

No abstract